Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell

Chapter 7. Linear Classifier and Logistic Regression

In the previous chapter, we added several useful supervised learning algorithms for regression tasks to our toolbox. Continuing with building on top of linear regression, in this chapter, we are going to build two classification algorithms: linear classifier and logistic regression. Both of them take familiar feature vectors as input, similar to multiple linear regression. The difference is in their output. The linear classifier will output true or false (binary classification) and logistic regression will provide the probability of some event happening.

The topics to discuss in this chapter are:

  • Bias and variance
  • Linear classifier
  • Logistic regression